机器学习确定 Mg2SiO4 的 P-T 相图
Machine learning determines the Mg2SiO4 P-T phase diagram
AI总结：
本研究利用机器学习势结合非平衡热力学积分和双相共存模拟，高效构建了 Mg2SiO4 的 P-T 相图，降低了计算成本并保持了热力学一致性。
摘要：
Mg2SiO4 及其高压多形体（瓦氏石和环woodite）之间的相变是地幔动力学和深地幔物质循环的核心问题。然而，这些相变边界的位置及其压力-温度（P-T）依赖性仍存在争议，主要是由于极端条件下的实验限制以及第一性原理自由能计算的高计算成本。本文提出了一种结合非平衡热力学积分（NETI）和双相共存模拟的机器学习势驱动工作流程，以实现大规模、长时间尺度的分子动力学采样。在此工作流程中，对于斯石英的熔化曲线被评估，并构建了完整的 P-T 相图。与传统 ab initio 方法相比，该策略在降低计算成本的同时保留了相稳定性评估中的热力学一致性。该工作流程适用于深地硅酸盐系统中相稳定性和热力学性质的高效评估。
英文摘要：
Phase transitions among Mg2SiO4 and its high-pressure polymorphs (wadsleyite and ringwoodite) are central to mantle dynamics and deep-mantle material cycling. However, the locations and Pressure-Temperature (P-T) dependences of these phase boundaries remain debated, largely due to experimental limitations at extreme conditions and the high computational cost of first-principles free-energy calculations. Here, a machine-learning-potential driven workflow combining non-equilibrium thermodynamic integration (NETI) and two-phase coexistence simulations is employed to enable large-scale, long-timescale molecular dynamics sampling. Within this workflow, the melting curve of forsterite is evaluated and a complete P-T phase diagram is constructed. Relative to conventional ab initio approaches, this strategy reduces computational expense while retaining thermodynamic consistency in phase-stability assessment. The workflow is applicable to efficient evaluation of phase stability and thermodynamic properties in deep-Earth silicate systems.
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